SIA OpenIR  > 机器人学研究室
Continuous Estimation of Human Multi-joint Angles from sEMG Using a State-space Model
Ding QC(丁其川); Han JD(韩建达); Zhao XG(赵新刚)
作者部门机器人学研究室
关键词Surface Electromyography (Semg) Closed-loop Estimation Multi-joint Movement Redundancy Segmentation
发表期刊IEEE Transactions on Neural Systems and Rehabilitation Engineering
ISSN1534-4320
2017
卷号25期号:9页码:1518-1528
收录类别SCI ; EI
EI收录号20173804195932
WOS记录号WOS:000410192400016
产权排序1
资助机构National Natural Science Foundation of China under Grant 61503374, 61573340 ; National High Technology Research and Development Program of China under Grant 2015AA042301 ; Liaoning Provincial Doctoral Starting Foundation of China under Grant 201501032 ; Self-planned Project of the State Key Laboratory of Robotics under Grant 2015-z06.
摘要Due to the couplings among joint-relative muscles, it is a challenge to accurately estimate continuous multi-joint movements from multi-channel sEMG signals. Traditional approaches always build a nonlinear regression model, such as artificial neural network, to predict the multi-joint movement variables using sEMG as inputs. However, the redundant sEMGdata are always not distinguished; the prediction errors cannot be evaluated and corrected online as well. In this work, a correlation-based redundancy-segmentation method is proposed to segment the sEMG-vector including redundancy into irredundant and redundant subvectors. Then, a general state-space framework is developed to build the motion model by regarding the irredundant subvector as input and the redundant one as measurement output. With the built state-space motion model, a closed-loop prediction-correction algorithm, i.e., the unscented Kalman filter (UKF), can be employed to estimate the multijoint angles from sEMG, where the redundant sEMG-data are used to reject model uncertainties. After having fully employed the redundancy, the proposed method can provide accurate and smooth estimation results. Comprehensive experiments are conducted on the multi-joint movements of the upper limb. The maximum RMSE of the estimations obtained by the proposed method is 0.160.03, which is significantly less than 0.250.06 and 0.270.07 (p<0.05) obtained by common neural networks.
语种英语
WOS标题词Science & Technology ; Technology ; Life Sciences & Biomedicine
WOS类目Engineering, Biomedical ; Rehabilitation
关键词[WOS]LIMB PROSTHESIS CONTROL ; ARTIFICIAL NEURAL-NETWORK ; ELECTROMYOGRAPHY ; EXTRACTION ; MOVEMENT ; SHOULDER ; MUSCLES
WOS研究方向Engineering ; Rehabilitation
引用统计
文献类型期刊论文
条目标识符http://ir.sia.cn/handle/173321/20237
专题机器人学研究室
通讯作者Ding QC(丁其川)
作者单位State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
推荐引用方式
GB/T 7714
Ding QC,Han JD,Zhao XG. Continuous Estimation of Human Multi-joint Angles from sEMG Using a State-space Model[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering,2017,25(9):1518-1528.
APA Ding QC,Han JD,&Zhao XG.(2017).Continuous Estimation of Human Multi-joint Angles from sEMG Using a State-space Model.IEEE Transactions on Neural Systems and Rehabilitation Engineering,25(9),1518-1528.
MLA Ding QC,et al."Continuous Estimation of Human Multi-joint Angles from sEMG Using a State-space Model".IEEE Transactions on Neural Systems and Rehabilitation Engineering 25.9(2017):1518-1528.
条目包含的文件 下载所有文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
Continuous Estimatio(3788KB)期刊论文作者接受稿开放获取ODC PDDL浏览 下载
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Ding QC(丁其川)]的文章
[Han JD(韩建达)]的文章
[Zhao XG(赵新刚)]的文章
百度学术
百度学术中相似的文章
[Ding QC(丁其川)]的文章
[Han JD(韩建达)]的文章
[Zhao XG(赵新刚)]的文章
必应学术
必应学术中相似的文章
[Ding QC(丁其川)]的文章
[Han JD(韩建达)]的文章
[Zhao XG(赵新刚)]的文章
相关权益政策
暂无数据
收藏/分享
文件名: Continuous Estimation of Human Multi-Joint Angles From sEMG Using a State-Space Model.pdf
格式: Adobe PDF
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。